The use of multilevel signals in an optical network architecture increases channel data throughput rates without requiring replacement of the existing optical fiber in a link (i.e., the optical fiber plant). While multilevel signals can offer this advantage of increased channel data throughput rates to an optical network architecture, conventional hardware and software used to decode the multilevel signal often cannot achieve this advantage because of difficulties in establishing thresholds by receivers for a multilevel signal. These thresholds are needed by the receiver to decode the multilevel signal into the one or more symbols that make up the multilevel signal.
The difficulties in establishing the thresholds are associated with reliably characterizing the noise that is present within a multilevel signal. Further, conventional hardware and software do not address multilevel signals comprising greater than two-level data streams. That is, the conventional art only provides methods for automatically controlling the threshold or decision points for traditional two-level data streams.
The voltage detection thresholds or decision points of multilevel receivers are usually centered in a statistical center of each of the troughs of a graphical representation of a marginal probability density function (pdf) that corresponds to the “eyes” of an “eye diagram” for a multilevel signal in order to minimize the number of decoding errors. Since the troughs or “eyes” of a pdf are usually not uniformly distributed in voltage, a simple conventional direct analog-to-digital conversion (ADC) at a minimum number of bits is not adequate for decoding a multilevel signal.
Conventional receivers for decoding two-level multilevel signals frequently assume additive noise with parametric noise distributions such as a Gaussian distribution. Conventional receivers for decoding two-level multilevel signals also usually assume simple linear dependencies on the transmitted two-level multilevel signal.
However, the noise in optical channels of a multilevel signal may have distributions that are non-Gaussian. Further, the distortion of multilevel signals may be nonlinear in nature resulting in asymmetric or multi-modal pdfs for the received signal.
In addition to the problems associated with estimating noise distributions in a multilevel signal, another problem exists in the conventional art with respect to reliably determining the fidelity of a received multilevel signal without the explicit transmission of a “testing” data sequence that is already known to the receiver. Conventional performance monitors can generally be categorized into one of two sets.
The first set are those that use a secondary threshold (or sampling phase) to approximately determine how often a received symbol is near to the primary threshold (or sampling phase) used for decoding. When the detected sample from this second threshold differs from the primary sample, a pseudo-error is said to have occurred. The link is then characterized with the pseudo-error rate. This class of approaches, however, neglects the fact that under optimal filtering, the primary and secondary samples will be heavily statistically correlated, and thus, misrepresents the link performance.
The second set of performance monitors of the conventional art are those that rely on acquiring statistics from an error correction module. Specifically, forward error correction coding is used at the transmitter to allow the receiver to correct a small number of errors. If the true number of errors incurred during transmission is sufficiently small, then the receiver can correct all of the errors and report the rate at which errors occur. This class of performance monitors, however, suffers two significant drawbacks.
First, these methods require the use of an error correction code so that errors can be detected. The second drawback is that transmission errors must occur in order to acquire statistics regarding their frequency of occurrence. By the very nature of the high quality of the link, these errors will rarely occur, and thus, the performance monitor requires a significant amount of time to reliably report the error rate.
In view of the foregoing, there exists a need in the art for a multilevel signal receiver that does not assume a particular noise distribution in a received multilevel signal. That is, a need exists in the art for a multilevel signal receiver that employs robust estimates of noise distributions in order to process complex signal distortions that may be present in a multilevel signal while maintaining high performance for classic Gaussian noise distributions that may also be present in a multilevel signal. Aspects include the need in the art for (1) a method and system for automatically selecting the decision thresholds for a multilevel signal receiver on an adaptive basis, (2) a multilevel signal receiver that can process multi-modal conditional probability density functions, and (3) a method and system for decoding multilevel signals that can provide a reliable fidelity measure of the received signal without the transmission of explicit error testing sequences known to the receiver. In other words, a need exists in the art for a complete statistical characterization of link noise to reliably establish decision thresholds and infer error rates without suffering from the aforementioned drawbacks of the conventional art.